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dc.contributor.authorBowring, James Frederick
dc.contributor.authorRehg, James Matthew
dc.contributor.authorHarrold, Mary Jean
dc.date.accessioned2005-03-28T15:07:59Z
dc.date.available2005-03-28T15:07:59Z
dc.date.issued2003
dc.identifier.urihttp://hdl.handle.net/1853/5937
dc.description.abstractA program's behavior is ultimately the collection of all its executions. This collection is diverse, unpredictable, and generally unbounded. Thus it is especially suited to statistical analysis and machine learning techniques. We explore the thesis that 1st- and 2nd-order Markov models of event-transitions are effective predictors of program behavior. We present a technique that models program executions as Markov models, and a clustering method for Markov models that aggregates multiple program executions, yielding a statistical description of program behaviors. With this approach, we can train classifiers to recognize specific behaviors emitted by an execution without knowledge of inputs or outcomes. We evaluate an application of active learning to the efficient refinement of our classifiers by conducting three empirical studies that explore a scenario illustrating automated test plan augmentation. We present a set of potential research questions and applications that our work suggests.en
dc.format.extent453796 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesCERCS;GIT-CERCS-03-19
dc.subjectClassification techniquesen
dc.subjectData clusteringen
dc.subjectEmpirical studiesen
dc.subjectEvent-transitionsen
dc.subjectMachine learningen
dc.subjectMarkov modelsen
dc.subjectModelingen
dc.subjectPredictorsen
dc.subjectProgram behavioren
dc.subjectProgram executionsen
dc.subjectStatistical analysisen
dc.subjectStatistical description of program behaviors
dc.titleSoftware Behavior: Automatic Classification and its Applicationsen
dc.typeTechnical Reporten


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